Tailoring Healthcare Analytics to a Value-Based Future

An industry expert says that new approaches to analytics will be needed to survive and thrive in tomorrow’s healthcare

In January 2015, the U.S. Department of Health and Human Services (HHS) boldly announced a plan to tie 30 percent of traditional fee-for-service, Medicare payments to quality or value through alternative payment models such as accountable care organizations (ACOs) and bundled payments by 2016, and tying 50 percent of payments to these models by the end of 2018. HHS also set a goal of tying 85 percent of all traditional Medicare payments to quality or value by 2016 and 90 percent by 2018 through initiatives such as the Hospital Value Based Purchasing and the Hospital Readmissions Reduction Programs.

But, earlier this year, a survey from Salt Lake City, Utah-based analytics vendor Health Catalyst revealed findings that many expected: most industry stakeholders seem to think the government was quite ambitious with these projected numbers. The survey, at the time of its publication, found that just 3 percent of health systems have already met the target set by HHS. Only 23 percent expect to meet it by 2019, just a year after the feds had hoped that half of all Medicare reimbursements would be value-based. What’s more, the majority of health systems—a full 62 percent—had either zero or less than 10 percent of their care tied to the type of risk-based contracts identified by HHS as “value-based,” including Medicare ACOs and bundled payments, the survey revealed.

The healthcare executives surveyed did say that they intend to steadily increase value-based care and at-risk contracts, and they said the most important organizational element needed for success with risk-based contracting is analytics. This is where Leonard D’Avolio, Ph.D., an assistant professor in the Brigham and Women’s division of general internal medicine and primary care, says change is needed. Dr. D’Avolio is also the CEO and co-founder of Cyft, a company based on years of his research optimizing machine learning and natural language processing to improve healthcare. He previously led informatics for the Department of Veterans Affairs’ (VA) precision medicine initiative (the Million Veteran Program) and the first clinical trial embedded within an electronic medical record (EMR) system.

D’Avolio fully understands that the success of value-based care is dependent on healthcare stakeholders understanding and predicting what will happen based on the information they have. Thus, he recommends a different approach to analytics from what has traditionally been practiced in healthcare. He says, “As value-based care organizations are now discovering, these multi-million dollar investments in traditional analytics are useful for understanding what happened—how many beds were filled, drugs prescribed, surgeries performed. However, they are incapable of answering the fundamental questions of value-based care: what should happen, to whom, when, and how, in order to prevent future events.”

As such, he says that most clinically relevant information is ignored by traditional analytics. To this end, as part of Healthcare Informatics’ Special Report on data analytics in this issue, D’Avolio recently spoke to Managing Editor Rajiv Leventhal about what needs to change in approaches to leveraging analytics in healthcare’s value-based future. Below are excerpts of that discussion.

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Can you tell me a little about your company, as it relates to the future of healthcare, and healthcare analytics?

Our company is focused on making technologies—such as machine learning and natural language processing—available to data analysts so they can harness the power of predictions in ways they haven’t been able to. We try to find organizations where the chief financial officer and the chief medical officer have the same incentive, meaning the organization is at financial risk for delivering high quality care. Frankly, relatively little of care provided at hospitals is at true financial risk today, though that number is increasing. Most companies are incentivized to still invest in technology to help them see folks more quickly. We are happy to see that changing though.

Sure you can talk about readmissions, but when you are at full financial risk, what you really care about is preventable utilization. Our customers will sometimes start the conversation asking about readmissions, but we ask them, what interventions do you have at your disposal? They might say that they hired a nurse to focus on COPD [chronic obstructive pulmonary disease]. So we say to them, what if we build a model to identify exactly who in your COPD population will end up in the ER in the near future? It’s a different approach from today’s risk scores, which is limited to claims data and is too one-sized-fits-all, with a focus on only a few problems. These approaches treat the geriatric patient with heart disease the same way as the high-risk pregnant patient. So we are trying to move away from one-sized-fits-all approaches.

Leonard D’Avolio, Ph.D.

How do you view the overall landscape in terms of analytics being leveraged by payers and providers as they move into risk-based contracting and reimbursing for value rather than volume?

The writing is on the wall; unless there is going to be a major political shift that comes with it a gutting of CMS [Centers for Medicare & Medicaid Services] policy, we are moving towards value-based care in various forms. CMS opened Pandora’s Box leading with ACOs, alternative payment models, and bundled payments, and the commercial plans have been waiting for this forever. When CMS fired that first shot with ACOs, many of the commercial plans turned to ACQs, or alternative quality contracts.

If you are having to do ACO models as part of your CMS reimbursement anyway, why not make it even more attractive and easier by giving more flexibility and creating alternative contracts so you can go at risk with us also? Most of the fee-for-service world is now keeping a close eye on MACRA [the Medicare Access and CHIP Reauthorization Act of 2015]. With MACRA, CMS and others drew a line in the sand saying that we will be more than 50 percent value-based by 2018.

One of the major challenges in value-based care is that we are in that quantum state in healthcare where there isn’t a value-based care policy; even the ACO program has many different reimbursement and quality measurement policies. There are a number of things with alternative payment models that need to be measured and reported on, too. It’s not getting talked about much, but healthcare is not transitioning to one new way of paying for care. In fact, depending on how you measure it, there are between five and 12 versions of this, many being implemented at the same time by provider organizations.

This leads to challenges around analytics for IT departments. With each new flavor of reimbursement usually comes a new layer of process measures that needs to be reported against, which usually means new bolt-ons to the EMR, which was never designed to improve the quality of care to begin with. So you are taking an EMR, which was designed 30 years ago for financial reimbursement, to communicate narratively between clinicians, and to ensure legal protection for the [provider], and you now bolt on dozens of new process measures. So you’re doing this quantum value-based care transition, and that creates challenges.

So what are the best analytics tools out there today?

In order to use analytics successfully, you want to take all information and turn it into actionable insight based on the organizations’ own highest priorities. Now, there is branching going on with analytics, driven by financial incentives. There are two branches that analytics are forced to operate within, and one of them is mandated reports based on each of your payer contracts. These are just reports, and they are mostly designed around the things that both sides agree on in advance and can probably be done based on using the EMRs we already have. The problem with mandating reporting is that we’re doing the opposite of what led to the digital transformation as experienced in other industries.

When other industries became digital, they had agreed upon outcomes, but then the competitive advantage came when they used all of their data to discover the best way to get to those outcomes. Amazon and Netflix, for example, did this by learning everything about the consumers they were serving. That’s the competitive advantage—taking all of the data and then becoming very personalized towards the recommendation and an agreed upon outcome. Healthcare has done the opposite in this branch of analytics—which is take all of the data we have, only look at a few points in time, create the standard patient and standard workflow, and somehow people think that will lead to the desired outcome.

The second branch of analytics is about organizations discovering the most efficient ways to do things. Because now, for the first time, you have to be able to make sense of all of the data, and you have to prioritize it for the care delivery folks. You can’t tell them that readmissions matter most to you. Instead, you have to say, “Here is the outcome—improve care—now you have to use your data to figure out the best pathways to get there.” In effect, you are becoming more like every other industry, in which digitization can reach its full potential. So I think if this happens, 95 percent of what passes as analytics today will either become obsolete or change dramatically.

Drilling down, regarding CMS’ readmissions reduction program, and the government’s mandatory bundled payment programs, how can I.T. leaders better use data analytics as they participate in these processes?

If you are going to work in the bundled payment world, you need to be able to anticipate and not react after the fact. So you have to become much narrower in your predictions. It’s not just about readmissions, but for example, which of my patients is most likely to end up with a non-routine discharge so I can begin to prepare that patient in the most cost effective pathway possible? That’s a specific example of something we are doing now, and we are finding that you need to be able to consume far more than just claims data in order to make those kinds of predictions. Any tool that uses claims data alone has one arm tied behind its back. The claims data is dated, and also, ICD-9 codes can be up to 70 percent inaccurate depending on the disease. This will only get worse as we move to the 65,000 disease codes of ICD-10.

Another thing we are involved in now that you wouldn’t think about in a traditional value-based sense is patient satisfaction. We’re working with a managed care organization around which members of their population are most likely to disenroll after being in the program for one year. From a CMO and CFO incentive perspective, if you are going to invest in keeping folks healthy for a year, and take on acquisition and health maintenance costs, then knowing who will leave after a year is a big deal. So we are taking on 30 different file types and helping this managed care organization predict who is likely to leave in a year. It’s not curing cancer, but it’s critical for organizations to survive. People will have to get more granular using all their data rather than one-sized-fits-all risk scores for readmissions.

What advice can you give to CIOs, CMIOs and CMOs as they continue to prepare for this new world?

First, analytics is not a tool; it’s a process. Clinicians understand where to focus, but you need to come up with the processes, tools, and support staff that will help and empower them to identify the highest priorities. Also, measure them on where it’s working and not working with ongoing feedback loops. It’s a problem to think of analytics as a product that you buy that will lead to behavior change, workflow change, and process change.

Second, be able to distinguish between what counts as analytics in the fee-for-service world with what will be required of analytics in a value-based world. You need to move beyond claims data and really use all of your data. It’s about understanding not just what happened, but what is most likely to happen and what you should be doing about it. This is very different than the traditional approach of what is considered analytics in healthcare.

Third, no CIO should settle for a vendor’s insistence about what’s good enough when it comes to predictions. If you are building models based on other peoples’ data and other peoples’ priorities and populations, you cannot presume that can be brought into your shop and will perform at the same level. CIOs need to own the evaluation with their own data on their own problems.

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Artificial intelligence (AI) has been a hot topic lately. Much has been said about its promise to improve our lives, as well as its threat to replace jobs ranging from receptionists to radiologists. These wider discussions have naturally led to some interesting questions about the future of medicine. What role will human beings have in an ever-changing technology landscape? When AI becomes a better "doctor," what will become of doctors? How will patients and medical professionals adjust to these changes?

While it is, of course, hard to make accurate predictions about the distant future, my experience both as a doctor and now CEO of a software company that uses AI to help doctors deliver safer care, gives me some insight into what the intermediate future will hold for the medical profession.

Medicine is one of the great professions in every culture in the world—an altruistic, challenging, aspirational vocation that often draws the best and the brightest. Doctors spend years in training to make decisions, perform procedures, and guide people through some of their most vulnerable points in life. But medicine is, for the most part, still stuck in a pre-internet era. Entering a hospital is like walking into a time capsule to a world where people still prefer paper, communication happens through pagers, and software looks like it’s from the 1980s or 1990s.

But this won’t last; three giant forces of technology have been building over the last few years, and they are about to fundamentally transform healthcare: the cloud, mobile, and AI. The force least understood by doctors is AI; after all, even technophobic doctors now spend a lot of time using the internet on their smartphones. Even so, AI is the one that will likely have the biggest impact on the profession.

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A lot of people believe that AI will become the primary decision maker, replacing human doctors. In that eventuality, Dr. AI will still need a human “interface,” because it is likely patients will need the familiarity of a human to translate the AI’s clinical decision making and recommendations. I find it an intriguing thought—going to the doctor’s office and seeing a human whose job it is to read the recommendations of a computer just to offer the human touch.

But to understand what the future could hold, we must first understand the different types of problems that need to be solved. Broadly, problems can be split into simple, complicated, and complex ones. Simple and complicated problems can be solved using paradigmatic thought (following standardized sets of rules), something computers excel at. What makes complex problems unique is that they require judgment based on more than just numbers and logic. For the time being, the modern machine learning techniques that we classify as “AI” are not well suited to solving complex problems that require this deeper understanding of context, systems, and situation.

Given the abundance of complex problems in medicine, I believe that the human “interfaces” in an AI-powered future won't simply be compassionate people whose only job is to sit and hold the hand of a patient while reading from a script. These people will be real doctors, trained in medicine in much the same way as today—in anatomy, physiology, embryology, and more. They will understand the science of medicine and the decision making behind Dr. AI. They will be able to explain things to the patient and field their questions in a way that only people can. And most importantly, they will be able to focus on solving complex medical problems that require a deeper understanding, aided by Dr. AI.

I believe that the intermediate future of medicine will feel very similar to aviation today. Nobody questions whether commercial airline pilots should still exist, even though computers and autopilot now handle the vast majority of a typical flight. Like these pilots, doctors will let "auto-doc" automate the routine busy work that has regrettably taken over a lot of a clinician’s day—automatically tackling simple problems that only require human monitoring, such as tracking normal lab results or following an evidence-based protocol for treatment. This will let doctors concentrate on the far more complex situations, like pilots do for takeoffs and landings.

Dr. AI will become a trusted assistant who can help a human doctor make the best possible decision, with the human doctor still acting as the ultimate decision maker. Dr. AI can pull together all of the relevant pieces of data, potentially highlighting things a human doctor may not normally spot in an ocean of information, while the human doctor can take into consideration the patient and their situation as a whole.

Medicine is both an art and a science, requiring doctors to consider context when applying evidence-based practices. AI will certainly take over the science of medicine in the coming years but most likely won't take over the art for a while. However, in the near future, doctors will need to evolve from being scientists who understand the art of medicine to artists who understand the science.

In comparing healthcare CIOs’ priorities at the end of 2017 to this current moment, new analysis has found that core clinical IT goals have shifted from focusing on EHR (electronic health record) integration to data analytics.

In December 2017, hospitals CIOs said they planned to mostly focus on EHR integration and mobile adoption and physician buy-in, according to a survey then-conducted by Springfield, Va.-based Spok, a clinical communications solutions company, of College of Healthcare Information Management Executives (CHIME) member CIOs.

The survey from one year ago found that across hospitals, 40 percent of CIO respondents said deploying an enterprise analytics platform is a top priority in 2018. Seventy-one percent of respondents cited integrating with the EHR is a top priority, and 62 percent said physician adoption and buy-in for securing messaging was a top priority in the next 18 months. What’s more, 38 percent said optimizing EHR integration with other hospital systems with a key focus for 2018.

Spok researchers were curious whether their predictions became reality, so they analyzed several industry reports and asked a handful of CIOs to recap their experiences from 2018. The most up-to-date responses revealed that compared to last year when just 40 percent of CIOs said they were deploying an enterprise analytics platform in 2018, harnessing data analytics looks to be a huge priority in 2019: 100 percent of the CIOs reported this as top of mind.

Further comparisons on 2018 predictions to realities included:

62 percent of CIOs predicted 2018 as the year of EHR integration; 75 percent reported they are now integrating patient monitoring data

79 percent said they were selecting and deploying technology primarily for secure messaging; now, 90 percent of hospitals have adopted mobile technology and report that it’s helping improve patient safety and outcomes

54 percent said the top secure messaging challenge was adoption/buy in; now, 51 percent said they now involve clinicians in mobile policy and adoption

What’s more, regarding future predictions, 87 percent of CIOs said they expect to increase spending on cybersecurity in 2019, and in three years from now, 60 percent of respondents expect data to be stored in a hybrid/private cloud.

CIOs also expressed concern regarding big tech companies such as Apple, Amazon and Google disrupting the healthcare market; 70 percent said they were somewhat concerned.

Managing clinical variation continues to be a significant challenge facing most hospitals and health systems today as unwarranted clinical variation often results in higher costs without improvements to patient experience or outcomes.

Like many other hospitals and health systems, Flagler Hospital, a 335-bed community hospital in St. Augustine, Florida, had a board-level mandate to address its unwarranted clinical variation with the goal of improving outcomes and lowering costs, says Michael Sanders, M.D., Flagler Hospital’s chief medical information officer (CMIO).

“Every hospital has been struggling with this for decades, managing clinical variation,” he says, noting that traditional methods of addressing clinical variation management have been inefficient, as developing care pathways, which involves identifying best practices for high-cost procedures, often takes up to six months or even years to develop and implement. “By the time you finish, it’s out of date,” Sanders says. “There wasn’t a good way of doing this, other than picking your spots periodically, doing analysis and trying to make sense of the data.”

What’s more, available analytics software is incapable of correlating all the variables within the clinical, billing, analytics and electronic health record (EHR) databases, he notes.

Another limitation is that care pathways are vulnerable to the biases of the clinicians involved, Sanders says. “In medicine, what we typically do is we’ll have an idea of what we want to study, design a protocol, and then run the trial and collect the data that we think is important and then we try to disprove or prove our hypothesis,” he says.

Working with Palo Alto, Calif.-based machine intelligence software company Ayasdi, Flagler Hospital initiated a pilot project to use Ayasdi’s clinical variation management application to develop care pathways for both acute and non-acute conditions and then measure adherence to those pathways.

Michael Sanders, M.D.

Flagler targeted their treatment protocols for pneumonia as an initial care process model. “We kicked around the idea of doing sepsis first, because it’s a huge problem throughout the country. We decided to use pneumonia first to get our feet wet and figure out how to use the tool correctly,” he says.

The AI tools from Ayasdi revealed new, improved care pathways for pneumonia after analyzing thousands of patient records from the hospital and identifying the commonalities between those with the best outcomes. The application uses unsupervised machine learning and supervised prediction to optimally align the sequence and timing of care with the goal of optimizing for patient outcomes, cost, readmissions, mortality rate, provider adherence, and other variables.

The hospital quickly implemented the new pneumonia pathway by changing the order set in its Allscripts EHR system. As a result, for the pneumonia care path, Flagler Hospital saved $1,350 per patient and reduced the length of stay (LOS) for these patients by two days, on average. What’s more, the hospital reduced readmission by 7 times—the readmission rate dropped from 2.9 percent to 0.4 percent, hospital officials report. The initial work saved nearly $850,000 in unnecessary costs—the costs were trimmed by eliminating labs, X-rays and other processes that did not add value or resulted in a reduction in the lengths of stay or readmissions.

With the success of the pneumonia care pathway, Flagler Hospital leaders also deployed a new sepsis pathway. The hospital has expanded its plans for using Ayasdi to develop new care pathways, from the original plan of tackling 12 conditions over three years, to now tackling one condition per month. Future plans are to tackle heart failure, total hip replacement, chronic obstructive pulmonary disease (COPD), coronary artery bypass grafting (CABG), hysterectomy and diabetes, among other conditions. Flagler Hospital expects to save at least $20 million from this program in the next three years, according to officials.

Finding the “Goldilocks” group

Strong collaboration between IT and physician teams has been a critical factor in deploying the AI tool and to continue to successfully implement new care pathways, Sanders notes.

The effort to create the first pathway began with the IT staff writing structured query language (SQL) code to extract the necessary data from the hospital’s Allscripts EHR, enterprise data warehouse, surgical, financial and corporate performance systems. This data was brought into the clinical variation management application using the FHIR (Fast Healthcare Interoperability Resources) standard.

“That was a major effort, but some of us had been data scientists before we were physicians, and so we parameterized all these calls. The first pneumonia care path was completed in about nine weeks. We’ve turned around and did a second care path, for sepsis, which is much harder, and we’ve done that in two weeks. We’ve finished sepsis and have moved on to total hip and total knee replacements. We have about 18 or 19 care paths that we’re going to be doing over the next 18 months,” he says.

After being fed data of past pneumonia treatments, the software automatically created cohorts of patients who had similar outcomes accompanied by the treatments they received at particular times and in what sequence. The program also calculated the direct variable costs, average lengths of stay, readmission and mortality rates for each of those cohorts, along with the statistical significance of its conclusions. Each group had different comorbidities, such as diabetes, COPD and heart failure, which was factored into the application's calculations. At the push of a button, the application created a care path based on the treatment given to the patients in each cohort.

The findings were then reviewed with the physician IT group, or what Sanders calls the PIT crew, to select what they refer to as the “Goldilocks” cohort. “This is a group of patients that had the combination of low cost, short length of stay, low readmissions and almost zero mortality rate. We then can publish the care path and then monitor adherence to that care path across our physicians,” Sanders says.

The AI application uncovered relationships and patterns that physicians either would not have identified or would have taken much longer to identify, Sanders says. For instance, the analysis revealed that for patients with pneumonia and COPD, beginning nebulizer treatments early in their hospital stays improved outcomes tremendously, hospital leaders report.

The optimal events, sequence, and timing of care were presented to the physician team using an intuitive interface that allowed them to understand exactly why each step, and the timing of the action, was recommended. Upon approval, the team operationalized the new care path by revising the emergency-department and inpatient order sets in the hospital EHR.

Sanders says having the data generated by the AI software is critical to getting physicians on board with the project. “When we deployed the tool for the pneumonia care pathway, our physicians were saying, ‘Oh no, not another tool’,” Sanders says. “I brought in a PIT Crew (physician IT crew) and we went through our data with them. I had physicians in the group going through the analysis and they saw that the data was real. We went into the EMR to make sure the data was in fact valid, and after they realized that, then they began to look at the outcomes, the length of stay, the drop in readmissions and how the costs dropped, and they were on board right away.”

The majority of Flagler physicians are adhering to the new care path, according to reports generated by the AI software's adherence application. The care paths effectively sourced the best practices from the hospital’s best doctors using the hospital’s own patient groups, and that is key, Sanders notes.

“When we had conversations with physicians about the data, some would say, ‘My patient is sicker than yours,’ or ‘I have a different patient population.’ However, we can drill down to the physician’s patients and show the physician where things are. It’s not based on an ivory tower analysis, it’s based on our own data. And, yes, our patients, and our community, are unique—a little older than most, and we have a lot of Europeans here visiting. We have some challenges, but this tool is taking our data and showing us what we need to pursue. That’s pretty powerful.”

He adds, “It’s been amazing to see physicians rally around this. We just never had the tool before that could do this.”

While Flagler Hospital is a small community hospital with fewer resources than academic medical centers or larger health systems—for example, the hospital doesn’t have a dedicated data scientist but rather uses its in-house informatics staff for this project—the hospital is progressive in its use of advanced analytics, according to Sanders.

“We’ve been able to do a lot of querying ourselves, and we have some sepsis predictive models that we’ve created and put into place. We do a lot of real-time monitoring for sepsis and central line-associated bloodstream infections,” he says. “Central line-associated bloodstream infections are a bane for all hospitals. In the past year and a half, since we’ve put in our predictive model, we’ve had zero bloodstream infections, and that’s just unheard of.”

Sanders and his team plan to continue to use the AI tool to analyze new data and adjust the care paths according to new discoveries. As the algorithms find more effective and efficient ways to deliver care that result in better outcomes, Flagler will continue to improve its care paths and measure the adherence of its providers.

There continues to be growing interest, and also some hype, around AI tools, but Sanders notes that AI and machine learning are simply another tool. “Historically, what we’ve done is that we had an idea of what we wanted to do, conducted a clinical trial and then proved or disproved the hypothesis, based on the data that we collected. We have a tool with AI which can basically show us relationships that we didn’t know even existed and answer questions that we didn’t know to ask. I think it’s going to open up a tremendous pathway in medicine for us to both reduce cost, improve care and really take better care of our patients,” he says, adding, “When you can say that to physicians, they are on board. They respond to the data.”